No Peak-End Rule for Simple Positive Experiences Observed in Children and Adults
Bibliographic record
Abstract
We investigated the tendency of children and adults to rely on the most intense and final moments when judging positive experiences, a heuristic known as the peak-end rule. This rule allows us to judge experiences quickly, but it can bias judgments. In three experiments involving various age groups (N = 988, ages 2–97), we attempted to replicate prior findings of a peak-end rule for small and simple positive experiences (e.g., receiving small gifts; Do, Rupert, & Wolford, 2008). Based on the original study and peak-end rule predictions, we hypothesized that individuals of all ages would be less satisfied with a highly desirable gift followed by a less desirable gift than with a highly desirable gift alone. We failed to observe the peak-end rule in preschoolers, school-aged children, younger adults, or older adults in any of the contexts we investigated. Our results show little support for positive peak-end rule effects and mark boundary conditions for the rule.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".